Opposition-Based Quantum Bat Algorithm to Eliminate Lower-Order Harmonics of Multilevel Inverters

Selective harmonic elimination (SHE) technique is used in power inverters to eliminate specific lower-order harmonics by determining optimum switching angles that are used to generate Pulse Width Modulation (PWM) signals for multilevel inverter (MLI) switches. Various optimization algorithms have been developed to determine the optimum switching angles. However, these techniques are still trapped in local optima. This study proposes an opposition-based quantum bat algorithm (OQBA) to determine these optimum switching angles. This algorithm is formulated by utilizing habitual characteristics of bats. It has advanced learning ability that can effectively remove lower-order harmonics from the output voltage of MLI. It can eventually increase the quality of the output voltage along with the efficiency of the MLI. The performance of the algorithm is evaluated with three different case studies involving 7, 11, and 17-level three-phase MLIs. The results are verified using both simulation and experimental studies. The results showed substantial improvement and superiority compared to other available algorithms both in terms of the harmonics reduction of harmonics and finding the correct solutions

REAL TIME GA AND ANN BASED SELECTIVE HARMONIC ELIMINATION IN 9 LEVEL UPS INVERTER

High quality power is very much critical and essential for medical, research and industrial applications to bring good quality results with accurate evaluation. Hence all the sensitive equipments and critical loads need to be provided with high quality and reliable power, where Uninterruptible Power Supplies (UPS) are mainly used to supply reliable power to these loads. Inverter is the main component of a UPS. In the recent times, by the advanced usage of semiconductor devices and non linear loads, harmonics are unavoidable. So the actual challenge for UPS is, under a non linear condition of load, it has to maintain a high quality sinusoidal output voltage. In this paper, the inverter of UPS is replaced by a nine level cascaded H bridge multilevel inverter with equal DC sources and harmonics can be eliminated by the optimal selection of switching angle by using Selective Harmonic Elimination Pulse Width Modulation (SHE-PWM) technique along with a hybrid technique to optimize and minimize the Total Harmonic Distortion(THD). The proposed hybrid technique utilizes the Genetic Algorithm (GA) and Neural Network (NN). The switching angles are calculated offline using Genetic Algorithm. Then the NN is trained by these switching angles and the real time switching angles are found out by Neural Network. The proposed technique is tested over a nine level cascaded H-bridge inverter and the resultant fundamental and harmonic voltages are analysed. Then, simulation is carried out in Matlab/Simulink environment and the results indicate that the switching angles obtained using this method results in efficient harmonic minimization

Recursive Selective Harmonic Elimination for Multilevel Inverters: Mathematical Formulation and Experimental Validation

A recursive method that eliminates +1 harmonics and their respective multiples from the output voltage of a cascaded H-bridge multilevel inverters with = 2 dc sources ( = 1, 2, 3,...) is proposed. It solves 2×2 linear systems with not singular matrices and always gives an exact solution with very low computational effort. Simulated results in three-phase five, nine, seventeen and thirty three level CHB inverters, and experimental results in five-level inverter demonstrate the validity of the method

Mitigation of Harmonics and Inter-Harmonics with LVRT and HVRT Enhancement in Grid-Connected Wind Energy Systems Using Genetic Algorithm-Optimized PWM and Fuzzy Adaptive PID Control

© 2021 Author(s). This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1063/5.0015579The growing installed wind capacity over the last decade has led many energy regulators to define specific grid codes for wind energy generation systems connecting to the electricity grid. These requirements impose strict laws regarding the Low Voltage Ride Though (LVRT) and High Voltage Ride Though (HVRT) capabilities of wind turbines during voltage disturbances. The main aim of this paper is to propose LVRT and HVRT strategies that allow wind systems to remain connected during severe grid voltage disturbances. Power quality issues associated with harmonics and inter-harmonics are also discussed and a control scheme for the grid-side converter is proposed to make the Wind Energy Conversion System insensitive to external disturbances and parametric variations. The Selective Harmonic Elimination Pulse Width Modulation technique based on Genetic Algorithm optimization is employed to overcome over-modulation problems, reduce the amplitudes of harmonics, and thus reduce the Total Harmonic Distortion in the current and voltage waveforms. Furthermore, to compensate for the fluctuations of the wind speed due to turbulence at the blades of the turbine, a fuzzy Proportional-Integral-Derivative controller with adaptive gains is proposed to control the converter on the generator side.Peer reviewedFinal Accepted Versio

Improved power quality operation of symmetrical and asymmetrical multilevel inverter using invasive weed optimization technique

Low switching frequency pulse width modulation (PWM) technique for modulation and control of multilevel inverter in medium voltage high power applications is preferred in order to reduce the switching losses. In this context, a multilevel inverter operated with Selective harmonics minimization PWM technique offers better quality waveform at reduced switching losses. After the Fourier series analysis, the system of non-linear simultaneous transcendental equations is obtained. These equations are then solved to obtain switching angles to have certain low order harmonics at minimum value and regulation in the fundamental voltage magnitude. In this paper, a novel invasive weed optimization (IWO) technique is proposed to compute switching angles. The proposed technique can compute switching angles for both symmetrical and asymmetrical multilevel inverters. Thus it has superiority over well-known optimization techniques such as GA, PSO, DE, and ACO, etc. Moreover, in certain modulation index ranges, it provides faster convergence and accurate results which have been demonstrated in the paper. The computational results have been verified with the experimental result on the prototype developed in the laboratory. The field programming gate arrays (FPGA) based controller is used to implement the proposed technique. The hardware results have been found in close agreement with the computed results. 2022This publication was made possible by NPRP grant #[ 13S-0108-20008 ] from the Qatar National Research Fund (A member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. The APC of the paper is funded by the Qatar National Library , Doha, Qatar.Scopu

Development and Implementation of Novel Intelligent Motor Control for Performance Enhancement of PMSM Drive in Electrified Vehicle Application

The demand for electrified vehicles has grown significantly over the last decade causing a shift in the automotive industry from traditional gasoline vehicles to electric vehicles (EVs). With the growing evolution of EVs, high power density, and high efficiency of electric powertrains (e–drive) are of the utmost need to achieve an extended driving range. However, achieving an extended driving range with enhanced e-drive performance is still a bottleneck. The control algorithm of e–drive plays a vital role in its performance and reliability over time. Artificial intelligence (AI) and machine learning (ML) based intelligent control methods have proven their continued success in fault determination and analysis of motor–drive systems. Considering the potential of intelligent control, this thesis investigates the legacy space vector modulation (SVM) strategy for wide–bandgap (WBG) inverter and conventional current PI controller for permanent magnet synchronous motor (PMSM) control to reduce the switching loss, computation time and enhance transient performance in the available state–of–the-art e–drive systems. The thesis converges on AI– and ML–based control for e–drives to enhance the performance by focusing in reducing switching loss using ANN–based modulation technique for GaN–based inverter and improving transient performance of PMSM by incorporating ML–based parameter independent controller